Article: "A comparative study on machine classification model in lung cancer cases analysis" Journal: Int. J. of Applied Systemic Studies, 2017 Vol.7 No.1/2/3 pp.13 - 29 Abstract: Due to the differences of machine classification models in the application of medical data, this paper selected different classification methods to study lung cancer data collected from HIS system with plenty of experiment and analysis, applying the R language on decision tree algorithm, bagging algorithm, Adaboost algorithm, conditions decision tree, random forests, naive Bayes, and neural network algorithm for lung cancer data analysis, in order to explore the advantages and disadvantages of each machine classification algorithm. The results confirmed that in lung cancer data research, naive Bayes, Adaboost algorithm and neural network algorithm have relatively high accuracy, with a good diagnostic performance. - Inderscience Publishers - linking academia, business and industry through research

Int. J. of Applied Systemic Studies » 2017 Vol.7, No.1/2/3

Title: A comparative study on machine classification model in lung cancer cases analysis

Authors: Jing Li; Zhisheng Zhao; Yang Liu; Jie Li; Zhiwei Cheng; Xiaozheng Wang

Addresses:
School of Information Science and Engineering, Hebei North University, Zhangjiakou, China
School of Information Science and Engineering, Hebei North University, Zhangjiakou, China
School of Information Science and Engineering, Hebei North University, Zhangjiakou, China
China-Japan Friendship Hospital, No. 2 East Yinghuayuan Street, Chaoyang District, Beijing, China
School of Information Science and Engineering, Hebei North University, Zhangjiakou, China
Zhangjiakou University, No. 19 Pingmen Road, Qiaoxi District, Zhangjiakou, China

Abstract: Due to the differences of machine classification models in the application of medical data, this paper selected different classification methods to study lung cancer data collected from HIS system with plenty of experiment and analysis, applying the R language on decision tree algorithm, bagging algorithm, Adaboost algorithm, conditions decision tree, random forests, naive Bayes, and neural network algorithm for lung cancer data analysis, in order to explore the advantages and disadvantages of each machine classification algorithm. The results confirmed that in lung cancer data research, naive Bayes, Adaboost algorithm and neural network algorithm have relatively high accuracy, with a good diagnostic performance.

Keywords: machine classification model; lung cancer; cross validation; R language; Adaboost algorithm; neural network; decision tree; conditions inference tree; random forest; naive Bayes; bagging algorithm.

DOI: 10.1504/IJASS.2017.10009756

Int. J. of Applied Systemic Studies, 2017 Vol.7, No.1/2/3, pp.13 - 29

Submission date: 22 Sep 2016
Date of acceptance: 05 Apr 2017
Available online: 18 Dec 2017